David Collett

Modelling Survival Data in Medical Research (eBook, PDF)

• Format: PDF

Produktbeschreibung

Fourth edition has new chapters on Bayesian survival analysis and use of the R software. Chapters extensively revised, expanded to add new material on topics that include methods for assessing predictive ability of a model, joint models for longitudinal and survival data, modern methods for the analysis of interval-censored survival data.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 556
• Erscheinungstermin: 31. Mai 2023
• Englisch
• ISBN-13: 9781000863109
• Artikelnr.: 68030422

Autorenporträt

David Collett obtained his first degree at the University of Leicester, before going on to complete an MSc in statistics at the University of Newcastle and a PhD in statistics at the University of Hull. David was a lecturer and senior lecturer in the Department of Applied Statistics at the University of Reading for over 25 years, including eight years as head of that department. In 2003, he was appointed Associate Director of Statistics and Clinical Studies at NHS Blood and Transplant. This involved supervising the statistical work of over 30 staff and collaborative work with transplant clinicians and research scientists. David became Director of the NHS Blood and Transplant Clinical Trials Unit, responsible for the design, conduct and analysis of clinical trials in transplantation and transfusion medicine. He also held a visiting chair in the Southampton Statistical Sciences Research Institute, University of Southampton, until his retirement. He now likes to spend as much time as possible on the golf course.

Inhaltsangabe

1. Survival analysis 2. Some non-parametric procedures 3. The Cox
regression model 4. Model checking in the Cox regression model 5.
Parametric regression models 6. Flexible parametric models 7. Model
checking in parametric models 8. Time-dependent variables 9.
Interval-censored survival data 10. Frailty models 11. Non-proportional
hazards and institutional comparisons 12 Competing risks 13. Multiple
events and event history modelling 14. Dependent censoring 15. Sample size
requirements for a survival study 16. Bayesian survival analysis 17.
Survival Analysis with R